An environment-aware Q-learning-based trust evaluation scheme inUnderwater Acoustic Sensor Networks (UASNs)

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F26%3A%230001279" target="_blank" >RIV/04274644:_____/26:#0001279 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/abs/pii/S0920548925001163?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/abs/pii/S0920548925001163?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.csi.2025.104087" target="_blank" >10.1016/j.csi.2025.104087</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An environment-aware Q-learning-based trust evaluation scheme inUnderwater Acoustic Sensor Networks (UASNs)

Popis výsledku v původním jazyce

Underwater acoustic sensor networks (UASNs) have diverse applications in military and civilian domains but are vulnerable to various security threats due to their broadcast nature and challenging underwater environment. Trust mechanisms have emerged as effective solutions to enhance security and reliability in UASNs. However, existing trust models often lack efficient trust update mechanisms that can manage inevitable dynamic fluctuations in the underwater environment and various potential attacks. In this paper, an environment-aware Q-learning-based trust evaluation (EAQTE) scheme is presented in UASNs. EAQTE incorporates environmental features such as communication channel quality and node stability into the trust computation. Communication quality is assessed based on the variance in successful packet transmission probability, while node stability is measured through movement similarity. Each node collects three types of trust evidence — energy-based, data-based, and communication-based — by interacting with neighboring nodes. Energy-based evidence includes residual energy, current energy change rate, and the similarity of energy change sequences to normal patterns. Data-based evidence evaluates the consistency of collected data, and communication-based evidence considers successful and unsuccessful interactions. EAQTE uses a Q-learning algorithm with three trust states (belief, disbelief, uncertainty) to dynamically adapt trust levels. Simulation results demonstrate that EAQTE improves detection accuracy by 7.01% compared to TUMRL, ARTMM, and TMC based on simulation time. However, under attack mode switching scenarios, EAQTE’s detection accuracy is approximately 2.86% lower than TUMRL. Additionally, EAQTE reduces the false alarm rate by 19.65% relative to TUMRL when node speed varies, and by 11.8% compared to TUMRL under different node densities. Furthermore, EAQTE achieves higher energy efficiency and improves it by 5.19% over TUMRL when the percentage of compromised nodes increases, and by approximately 5.66% across varying node densities. These results indicate that EAQTE effectively balances adaptability, accuracy, and energy consumption in challenging underwater environments.

Název v anglickém jazyce

An environment-aware Q-learning-based trust evaluation scheme inUnderwater Acoustic Sensor Networks (UASNs)

Popis výsledku anglicky

Underwater acoustic sensor networks (UASNs) have diverse applications in military and civilian domains but are vulnerable to various security threats due to their broadcast nature and challenging underwater environment. Trust mechanisms have emerged as effective solutions to enhance security and reliability in UASNs. However, existing trust models often lack efficient trust update mechanisms that can manage inevitable dynamic fluctuations in the underwater environment and various potential attacks. In this paper, an environment-aware Q-learning-based trust evaluation (EAQTE) scheme is presented in UASNs. EAQTE incorporates environmental features such as communication channel quality and node stability into the trust computation. Communication quality is assessed based on the variance in successful packet transmission probability, while node stability is measured through movement similarity. Each node collects three types of trust evidence — energy-based, data-based, and communication-based — by interacting with neighboring nodes. Energy-based evidence includes residual energy, current energy change rate, and the similarity of energy change sequences to normal patterns. Data-based evidence evaluates the consistency of collected data, and communication-based evidence considers successful and unsuccessful interactions. EAQTE uses a Q-learning algorithm with three trust states (belief, disbelief, uncertainty) to dynamically adapt trust levels. Simulation results demonstrate that EAQTE improves detection accuracy by 7.01% compared to TUMRL, ARTMM, and TMC based on simulation time. However, under attack mode switching scenarios, EAQTE’s detection accuracy is approximately 2.86% lower than TUMRL. Additionally, EAQTE reduces the false alarm rate by 19.65% relative to TUMRL when node speed varies, and by 11.8% compared to TUMRL under different node densities. Furthermore, EAQTE achieves higher energy efficiency and improves it by 5.19% over TUMRL when the percentage of compromised nodes increases, and by approximately 5.66% across varying node densities. These results indicate that EAQTE effectively balances adaptability, accuracy, and energy consumption in challenging underwater environments.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2026

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computer Standards & Interfaces

ISSN

0920-5489

e-ISSN

—

Svazek periodika

96

Číslo periodika v rámci svazku

March

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

001607241600001

EID výsledku v databázi Scopus

2-s2.0-105022209394